Data Tables

Raw Data

Subset of data used for visualization (Showing top 10 rows)

Mutilateral Environmental Agreements 1857 - 2016

mitch_id	Year signed	Year ratified	Subject	Lineage
2584	1877	1877	Species	Rhine - Lake Constance Fishing
2585	1878	1880	Species	International Plant Protection
2586	1881	1882	Species	International Plant Protection
2587	1884	2899	Species	Rhine - Lake Constance Fishing
2227	1885	1886	Freshwater	Congo River Basin
2588	1885	1886	Species	Rhine - Lake Constance Fishing
773	1990	1992	Freshwater	Elbe Pollution
817	1991	1993	Freshwater	Elbe Pollution
2489	1992	1992	General	CIS Environmental Agreements
1556	2000	2006	Freshwater	Danube
Data from Ronald B. Mitchell. 2002-2020. International Environmental Agreements Database Project

Visualization

Change in number of agreements over time

Regular updating needed

Ratification rate

dp3 <- data %>%
  select("subject_category", "tsig_yr", "teif_yr") %>%
  mutate(year_gap = teif_yr - tsig_yr) %>% 
  # filter(year_gap > 100)
  filter(year_gap < 100) %>% 
  group_by(year_gap) %>% # 349 year_gap = 0 
  count() %>% 
  summary(avg_gap = mean(year_gap)) #???
# mean 2.236
  
  sameyear = function(...) return(349)
  meanyear = function(...) return(2.2)
  never = function(...) return(141)

Ratified in the same year signed

ratifysameyear = sameyear()
valueBox(ratifysameyear, icon = "fa-clock-o")

349

Average year from signed to ratified

avgyearbetween = meanyear()
valueBox(avgyearbetween, icon = "fa-pause")

2.2

Never ratified

neverratify = never()
valueBox(neverratify, icon = "fa-ban")

141

Column

Chart C

# Total
nrow(data) #1311

[1] 1311

# consent to be bound 448/1311
data %>% 
  select(t_more_research, t_tap_clause1, region_type, region_continent, eif_type1) %>% 
  group_by(eif_type1) %>% 
  count(eif_type1)

# A tibble: 8 x 2
# Groups:   eif_type1 [8]
  eif_type1                    n
  <chr>                    <int>
1 ""                         745
2 "#N/A"                       8
3 "Consent to be bound"      448
4 "EIF absent"                 2
5 "Follows agreement"          1
6 "Missing agreement text"     2
7 "On signature"             103
8 "Tacit"                      2

# Politically adaptive 36
data %>% 
  select(t_more_research, t_tap_clause1, region_type, dynamic, eif_type1) %>% 
  group_by(dynamic) %>% 
  count(dynamic)

# A tibble: 6 x 2
# Groups:   dynamic [6]
  dynamic                      n
  <chr>                    <int>
1 ""                          20
2 "#N/A"                     737
3 "Permanent"                296
4 "Politically adaptive"      36
5 "Substantively adaptive"   192
6 "Unsure"                    30

# Regular updating needed 264
data %>% 
  select(t_more_research, t_tap_clause1, region_type, region_continent, eif_type1) %>% 
  group_by(t_more_research) %>% 
  count(t_more_research)

# A tibble: 6 x 2
# Groups:   t_more_research [6]
  t_more_research                    n
  <chr>                          <int>
1 ""                                20
2 "Incomplete information"          10
3 "Low priority"                   131
4 "Regular updating needed"        264
5 "Uncertain action information"    27
6 "Update not needed"              859